Build a predictive model that estimates house prices based on various features such as square footage, location, and number of rooms. This project focuses on using Scikit-learn’s regression algorithms to develop accurate models. You'll explore multiple regression techniques, evaluate model performance, and fine-tune for better predictions.

In this project, you'll analyze COVID-19 data from global or local sources to visualize trends in infections, recoveries, and deaths over time. By using time series analysis, you'll predict future trends and understand the factors that influence the spread. Visualizations with Seaborn and Matplotlib will help in presenting these trends clearly and effectively.

This project focuses on predicting customer churn for a subscription-based service by analyzing usage and demographic data. Using classification algorithms like logistic regression, decision trees, or random forests in Scikit-learn, you'll identify the key factors that influence customer retention. You'll also evaluate model performance with various metrics like accuracy, precision, and recall.

Conduct a detailed EDA on a publicly available dataset, such as the Titanic dataset. You'll clean the data, handle missing values, and explore distributions, correlations, and outliers. This project will teach you how to derive meaningful insights from raw data and create engaging visualizations using Seaborn and Matplotlib.

Analyze stock market data to identify key trends and predict future stock prices using regression or time series forecasting. By applying models like ARIMA or linear regression, you’ll forecast stock prices based on historical data. You’ll also visualize the stock trends and volatility to make better-informed predictions.

In this project, you'll apply clustering techniques like K-means, DBSCAN, or hierarchical clustering to group customers based on their purchasing behavior. This will help uncover hidden patterns in the data, such as customer preferences and trends. Visualizing the clusters will allow for more targeted marketing strategies and personalized customer experiences.

Analyze employee performance data to identify the key factors that impact productivity and satisfaction in the workplace. Using classification models from Scikit-learn, you'll predict employee attrition and understand the reasons behind employee turnover. This project will provide valuable insights for HR departments to reduce attrition rates and improve performance.

Build a model to predict the likelihood of loan default by analyzing financial data, including income, credit scores, and loan history. Using classification algorithms, you’ll assess the risk factors that contribute to loan defaults. Visualizations will help you interpret model results and communicate the findings effectively to stakeholders.

In this project, you’ll analyze traffic accident data to uncover patterns like accident hotspots, time of day, weather conditions, and road types. Using classification algorithms, you’ll predict the likelihood of accidents occurring under certain conditions. This analysis can help in understanding and mitigating traffic risks to improve public safety.